PREDICTION OF STOCK EXCHANGE ISTANBUL INDEX (BIST 100) RETURN VOLATILITY WITH ARCH AND GARCH MODELS
Journal Title: Muhasebe ve Vergi Uygulamaları Dergisi - Year 2018, Vol 10, Issue
Abstract
Due to the increasing volatility movements as the capital markets develop day by day, the globalization movement, the increase in the types of risk and the increasing uncertainty as the resulting markets become more complex, the analysis of stock market structures has become more important. Increasing volatility due to the leverage effects, asymmetry, etc. qualities in the financial series, could prevent the effective pricing of stocks in the stock markets. Especially developing countries have higher levels of openness and fragility. So it has great importance to establish the concept of volatility in securities exchanges in developing countries. In concurrence with the development in information communication technologies, the possibility of trading 24 hours/a day on the market has emerged. Estimating the volatility variable, which has recently become the most important variable in the investment decisions of the global investor, has become even more important especially in developing countries, since they are more fragile than developed countries. Estimating this variable has become even more important, especially for decision-makers who are considering investing in or partnering with the company. Since the conventional models are insufficient to express the volatility variable; ARCH, GARCH, EGARCH and TGARCH models, which are nonlinear conditional variance models, have begun to be used. The aim of the study is to analyze the return volatility of the BIST 100 Index by the ARCH, GARCH, EGARCH and TGARCH models. BIST 100 Index, which covers the 2011-2017 / 3 period with its daily closing values, are taken into account. As a result of the study, it was observed that TGARCH model, which has the highest level of explanatory power, gave the most successful results among related models in revealing BIST 100 return volatility.
Authors and Affiliations
Serdar KUZU
Keywords
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- EP ID EP414751
- DOI 10.29067/muvu.384418
- Views 102
- Downloads 0
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